I have a small gatekeeper.py script the agent has to run to complete a round or begin a new one. One of the things it does (in addition to record keeping, linting, etc) is validate that the wiki has been updated with all the findings, lessons learned, and documentation for the round. The agent can't proceed until it's done.
Then in the AGENTS.md they're told they can use QMD to perform semantic search of the wiki , and should before starting any significant work.
It's expensive, in terms of tokens, but well worth it. I could reproduce any stage of my project if I wanted. I always know why they did what they did (if they do), and the agent's can't really forget anything important.
mapontosevenths
I use a single Knowledge Base as the starting point of each and every AI session.
That knowledge Base doubles down as an Obsidian vault in which I also have various databases, plugins, etc).
My knowledge base contains notes about my goals, projects, tasks, products, ideas, etc. It also contains my articles (dsebastien.net), my tools and infrastructure, my open source and private projects with their repositories, specific instructions for each, etc.
In addition, I have built tools to help understand the taxonomy/ontology of my knowledge base, know which types of notes exist, which properties, which template to use, where to store what, etc.
On the human side, everything is automated, which lets me capture efficiently. On the AI side, all the knowledge is easy to find and leverage.
The whole knowledge base is like composable context for AI.
I can start sessions with sentences like "let's work on the music practice plugin; add xxxx" and AI will understand what project it is, where to find the sources, where to clone or update the repo on my machine, etc. And from the context within the repository itself, it will then know how the code is organized etc. The nice thing is that this also enables me to work on multiple projects in parallel easily; either in different AI sessions, or in a single session for work that spans multiple projects/repositories. For instance I have a repo which a product, another with the product documentation. When I ask for something in the context of that project, AI works seamlessly on both.
Use my Johnny.Decimal system. [0] (Disclosure: I'm Johnny.)
Every part of my life and business has a well-defined ID. This ID has:
- A defining entry in my index [1], which is an Obsidian vault.
- Therefore, also a Markdown note(s).
- A folder on disk.
I have a CLI [2] and agent skills [3] that help your LLM understand these relationships.
Then you just:
jd 21.15 // or search by title
claude
and now Claude has access to:
- All of your notes from your index. This is your shared memory: it's designed to be human-readable.
- All of its memory and transcripts from the fact that you launch it in the same folder whenever you do this piece of work. This is its internal memory.
- With this comes the ability to easily `--resume` past conversations and so on.
That's … it. Carefully-named Markdown files and folders. My numbers aren't a prerequisite – there's nothing magical about them – but they sure help.
You can do this all for free. It takes about half an hour to set up. My MCP server [4] adds some quality-of-life but is paid (and means I can buy lunch :).
> I’m exploring whether to build something
I really wouldn't. I get 10 emails a week from people who already are and I don't see the point. It's a solved problem.
I built https://pribrain.com for this and I'm happy to share a free trial with anyone who's interested.
Effectively I just need to connect the MCP and set the preferences/rules in the agent (codex, claude, opencode, etc.) to: "Read instructions and rules from PriBrain before doing anything." Its pricing is targeted at startups/SMBs, not freelancers, though (but we could arrange something if you're interested).
You can achieve a similar result with a markdown file and change "PriBrain" to that file path, then you just need to manage the core rules/instructions in a single place.
A great thing I leverage with PriBrain is using different PriBrain accounts per client, so each agent only gets the core rules/information for each client.
To your specific questions:
1. Just what's necessary. If it is a long wall of text I'll finish with "Read PriBrain first." just to make sure that initial instruction won't be ignored, because sometimes Claude and Codex will ignore them.
2. Yes. Similar answer, the shared brain.
3. I use separate Spaces (think of them as directories or some other kind of taxonomy).
4. Happens many times, but it's not related to the shared brain and that's why I verify work.
5. I used gbrain before and it still caused a lot of hallucinations, so I built my own solution.
Hopefully that helps.
BrunoBernardino
I've been trying different approaches to tackle agent memory across different sessions, without bloating context and increasing token spending.
I was using LLM Wiki for a while, it seemed to work well but when I decided to measure token usage, I ran into a surprising +23% in token usage (model was often double checking or query was finding more matches than intended).
I also tried static graphs for codebase structure, like graphify, and saw a +5% increase in token usage in the same benchmark.
It's a simple rust application that build a knowledge graph on top of the LLM wiki, with metadata that helps the model navigate through the markdown files, tracking files that are up to date and the structural relationships inside the project. It also led to -30% token usage on the same benchmarks both LLM Wiki and graphify increased the token usage.
For managing context between different teams, I use my memory by project, so personal and professional work is kept separate, and I just reuse templates as needed (for agent prompts for example).
zaterka
As many have said - my strong steer build something[^1].
There are _great_ tools for this, but the biggest part of this is managing your own attention, and mental load. You can craft something that really helps _you_ manage all the spinning plates.
In the case of my harness, I hoover up all the PR state into one place and then have a bunch of rules on how I interact with them (or not). Even down to a "swipe right to merge" card system. Works for me.
My recommendation: A cloudflare PWA fully vibe coded with zerotrust on the front. Don't get too attached, tools are changing fast. Make it work and be prepared to ditch it when something better comes along.
- I'm currently using Sentry's dotagents to share skills and pin versions
- I avoid a centralized "single brain" and instead have started a "knowledge" directory in each repo. I copy relevant learnings in so stuff stays isolated
- I use an OSS tool from my company to turn anything that could be enforced by code into something that can be enforced by code (keeps the markdown files leaner)
- My daily driver is cmux with a silly amount of sessions, so I'm constantly throwing away sessions and keeping context small
codedrift_
When I start a new project, I point to other ones where I'm happy with the quality as exemplars. I think its probably simpler to copy relevant stuff allow older projects go out of date until I start working on them again, then I tell the agent to look at the newer project to update its style.
I have 2-4 on going projects I tend to a bit of that, normally 2 at a time, when I pick up one of the others, I really just do that; point to the older one.
But within projects I don't really try to manage context, more so I just have an issue tracker which I add to keep track of stuff. In 2 of my long running projects I have some bootleg issue tracker built in (along with a screenshot QA tool which sends issues to the tracker) into the UI of the tool.
I guess to extent I can, I try to avoid keeping some preferences in the context like formatting and linting (including directory structure and white listing imports in certain parts). I've been trying to find more ways to automate that way, so I keep my prompts and skills and goals limited to objectives where possible, so preferences like styles and structure are automatically enforced.
When I have multiple agents, I just tell them to maintain write only ledgers to coordinate to avoid overlapping work, but it's easier when your project is broken up into self contained components with communication boundaries (defined in protobuf) like a FE/BE split or seperate service codebases (I reassemble it as a google style modular monolith to avoid running too many processes)
akst
I’m using obsidian vaults.
Currently I’m not collaborating with people a lot, so I use helpful plugins like Kanban to organize tasks.
This helps me stay in the loop about projects and tasks, since Claude can create requirements.
A friend of mine recently built campus [1] and while I personally haven’t gotten to use it, I’m currently trying to rebuild it as an obsidian plugin, which I hope will further help me stay organized.
comments (10)
I have a small gatekeeper.py script the agent has to run to complete a round or begin a new one. One of the things it does (in addition to record keeping, linting, etc) is validate that the wiki has been updated with all the findings, lessons learned, and documentation for the round. The agent can't proceed until it's done.
Then in the AGENTS.md they're told they can use QMD to perform semantic search of the wiki , and should before starting any significant work.
It's expensive, in terms of tokens, but well worth it. I could reproduce any stage of my project if I wanted. I always know why they did what they did (if they do), and the agent's can't really forget anything important.
mapontosevenths
My knowledge base contains notes about my goals, projects, tasks, products, ideas, etc. It also contains my articles (dsebastien.net), my tools and infrastructure, my open source and private projects with their repositories, specific instructions for each, etc.
In addition, I have built tools to help understand the taxonomy/ontology of my knowledge base, know which types of notes exist, which properties, which template to use, where to store what, etc.
On the human side, everything is automated, which lets me capture efficiently. On the AI side, all the knowledge is easy to find and leverage.
The whole knowledge base is like composable context for AI.
I can start sessions with sentences like "let's work on the music practice plugin; add xxxx" and AI will understand what project it is, where to find the sources, where to clone or update the repo on my machine, etc. And from the context within the repository itself, it will then know how the code is organized etc. The nice thing is that this also enables me to work on multiple projects in parallel easily; either in different AI sessions, or in a single session for work that spans multiple projects/repositories. For instance I have a repo which a product, another with the product documentation. When I ask for something in the context of that project, AI works seamlessly on both.
BTW this product of mine is a 1:1 copy of what I use for myself: https://store.dsebastien.net/osk
dSebastien
Every part of my life and business has a well-defined ID. This ID has:
- A defining entry in my index [1], which is an Obsidian vault.
- Therefore, also a Markdown note(s).
- A folder on disk.
I have a CLI [2] and agent skills [3] that help your LLM understand these relationships.
Then you just:
and now Claude has access to:- All of your notes from your index. This is your shared memory: it's designed to be human-readable.
- All of its memory and transcripts from the fact that you launch it in the same folder whenever you do this piece of work. This is its internal memory.
- With this comes the ability to easily `--resume` past conversations and so on.
That's … it. Carefully-named Markdown files and folders. My numbers aren't a prerequisite – there's nothing magical about them – but they sure help.
You can do this all for free. It takes about half an hour to set up. My MCP server [4] adds some quality-of-life but is paid (and means I can buy lunch :).
> I’m exploring whether to build something
I really wouldn't. I get 10 emails a week from people who already are and I don't see the point. It's a solved problem.
[0]: https://johnnydecimal.com/
[1]: https://johnnydecimal.com/documentation/the-jdex
[2]: https://johnnydecimal.com/documentation/jd-cli
[3]: https://johnnydecimal.com/documentation/agent-skills
[4]: https://johnnydecimal.com/software/mcp-server
jen729w
orbis-tertius
Effectively I just need to connect the MCP and set the preferences/rules in the agent (codex, claude, opencode, etc.) to: "Read instructions and rules from PriBrain before doing anything." Its pricing is targeted at startups/SMBs, not freelancers, though (but we could arrange something if you're interested).
You can achieve a similar result with a markdown file and change "PriBrain" to that file path, then you just need to manage the core rules/instructions in a single place.
A great thing I leverage with PriBrain is using different PriBrain accounts per client, so each agent only gets the core rules/information for each client.
To your specific questions:
1. Just what's necessary. If it is a long wall of text I'll finish with "Read PriBrain first." just to make sure that initial instruction won't be ignored, because sometimes Claude and Codex will ignore them.
2. Yes. Similar answer, the shared brain.
3. I use separate Spaces (think of them as directories or some other kind of taxonomy).
4. Happens many times, but it's not related to the shared brain and that's why I verify work.
5. I used gbrain before and it still caused a lot of hallucinations, so I built my own solution.
Hopefully that helps.
BrunoBernardino
I was using LLM Wiki for a while, it seemed to work well but when I decided to measure token usage, I ran into a surprising +23% in token usage (model was often double checking or query was finding more matches than intended). I also tried static graphs for codebase structure, like graphify, and saw a +5% increase in token usage in the same benchmark.
In the end, I created this hybrid solution, called wayfinder: https://github.com/zaterka/wayfinder
It's a simple rust application that build a knowledge graph on top of the LLM wiki, with metadata that helps the model navigate through the markdown files, tracking files that are up to date and the structural relationships inside the project. It also led to -30% token usage on the same benchmarks both LLM Wiki and graphify increased the token usage.
For managing context between different teams, I use my memory by project, so personal and professional work is kept separate, and I just reuse templates as needed (for agent prompts for example).
zaterka
There are _great_ tools for this, but the biggest part of this is managing your own attention, and mental load. You can craft something that really helps _you_ manage all the spinning plates.
In the case of my harness, I hoover up all the PR state into one place and then have a bunch of rules on how I interact with them (or not). Even down to a "swipe right to merge" card system. Works for me.
My recommendation: A cloudflare PWA fully vibe coded with zerotrust on the front. Don't get too attached, tools are changing fast. Make it work and be prepared to ditch it when something better comes along.
[1] https://jonathannen.com/build-your-own-lightsaber/
jwilliams
- I'm currently using Sentry's dotagents to share skills and pin versions
- I avoid a centralized "single brain" and instead have started a "knowledge" directory in each repo. I copy relevant learnings in so stuff stays isolated
- I use an OSS tool from my company to turn anything that could be enforced by code into something that can be enforced by code (keeps the markdown files leaner)
- My daily driver is cmux with a silly amount of sessions, so I'm constantly throwing away sessions and keeping context small
codedrift_
I have 2-4 on going projects I tend to a bit of that, normally 2 at a time, when I pick up one of the others, I really just do that; point to the older one.
But within projects I don't really try to manage context, more so I just have an issue tracker which I add to keep track of stuff. In 2 of my long running projects I have some bootleg issue tracker built in (along with a screenshot QA tool which sends issues to the tracker) into the UI of the tool.
I guess to extent I can, I try to avoid keeping some preferences in the context like formatting and linting (including directory structure and white listing imports in certain parts). I've been trying to find more ways to automate that way, so I keep my prompts and skills and goals limited to objectives where possible, so preferences like styles and structure are automatically enforced.
When I have multiple agents, I just tell them to maintain write only ledgers to coordinate to avoid overlapping work, but it's easier when your project is broken up into self contained components with communication boundaries (defined in protobuf) like a FE/BE split or seperate service codebases (I reassemble it as a google style modular monolith to avoid running too many processes)
akst
Currently I’m not collaborating with people a lot, so I use helpful plugins like Kanban to organize tasks.
This helps me stay in the loop about projects and tasks, since Claude can create requirements.
A friend of mine recently built campus [1] and while I personally haven’t gotten to use it, I’m currently trying to rebuild it as an obsidian plugin, which I hope will further help me stay organized.
[1] - https://campus.flutterflow.io/
encruxx